cerberus237/adaptable-teastore-recommender repository overview

⁠Adaptable TeaStore Recommender Service Documentation

⁠1. Overview of the Component

⁠Description

The Adaptable TeaStore Recommender service is responsible for providing personalized product recommendations to users based on their preferences and behaviors. This component leverages various algorithms to optimize the recommendation process, adapting dynamically to changing workloads.

⁠Architecture

The Recommender service operates as a microservice within the Adaptable TeaStore ecosystem. It communicates with other services like the Persistence service to fetch user data and product information. The architecture ensures that it can scale independently and respond to traffic variations without affecting other components.

⁠Role in the Adaptable TeaStore

• Recommendation Logic: Implements algorithms to generate personalized recommendations.
• Adaptability: Supports dynamic switching between different operational modes (e.g., high-performance, low-power).
• Integration: Works in conjunction with other services for data retrieval and processing.

⁠2. Helpful Docker Command

To start the Adaptable TeaStore Recommender service, you can use the following command:

docker run -e "REGISTRY_HOST=10.1.2.3" -e "REGISTRY_PORT=10000" -e "HOST_NAME=10.1.2.30" -e "SERVICE_PORT=3333" -p 3333:8080 -d cerberus237/adaptable-teastore-recommender

⁠Explanation of Command:

• docker run: Command to create and run a new container.
• -e "REGISTRY_HOST=10.1.2.3": Sets the environment variable for the registry host.
• -e "REGISTRY_PORT=10000": Sets the environment variable for the registry port.
• -e "HOST_NAME=10.1.2.30": Defines the host name for the service.
• -e "SERVICE_PORT=3333": Sets the port number for the service.
• -p 3333:8080: Maps port 3333 of the host to port 8080 of the container.
• -d: Runs the container in detached mode.
• cerberus237/adaptable-teastore-recommender: The name of the Docker image.

⁠3. Sample Docker-Compose Code

Here’s a sample docker-compose.yml file to run the Adaptable TeaStore Recommender service using Docker Compose:

version: '3'
services:
  recommender:
    image: cerberus237/adaptable-teastore-recommender
    expose:
      - "8080"
    environment:
      HOST_NAME: "recommender"
      REGISTRY_HOST: "registry"

⁠Explanation of Directives:

• version: '3': Specifies the Docker Compose file format version.
• services: Defines the services to be run.
  • recommender: The name of the service.
    • image: Specifies the Docker image to use for the service.
    • expose: Makes the specified port accessible to linked services (not to the host).
    • environment: Sets environment variables needed for the service.

⁠4. Overview of the Dockerfile

The Dockerfile for the Adaptable TeaStore Recommender service is designed to set up the environment for running the service. Below is an overview of its structure and components:

FROM cerberus237/adaptable-teastore-base:latest
LABEL org.opencontainers.image.authors="Inria R&D engineer <[email protected]>"

COPY target/*.war /usr/local/tomcat/webapps/

⁠Explanation of Dockerfile Components:

• FROM cerberus237/adaptable-teastore-base:latest: Specifies the base image to use, which is the latest version of the Adaptable TeaStore base image.
• LABEL: Provides metadata about the image, including the author's contact information.
• COPY target/*.war /usr/local/tomcat/webapps/: Copies the compiled WAR file of the Recommender service to the Tomcat webapps directory, making it available for deployment.

⁠5. Instrumented Adaptation Actions and Metrics Collectors

⁠5.1 Available Collectors

The recommender service provides a family of collectors to support various system metrics:

Each collector is designed to either:

• Collect local system metrics from a running service.
• Fetch remote metrics via REST API calls.

⁠5.2 Usage Examples

⁠Local CPU Usage Collection

The LocalCpuUsageCollector retrieves CPU usage metrics from the Java Management Extensions (JMX).

⁠Example: Collecting Local CPU Usage

LocalCpuUsageCollector cpuUsageCollector = new LocalCpuUsageCollector();
double currentCpuUsage = cpuUsageCollector.get();
System.out.println("Current CPU Usage: " + currentCpuUsage + "%");

⁠Remote CPU Usage Collection

The RemoteCpuUsageCollector fetches CPU metrics from a remote service using a REST API call.

RemoteCpuUsageCollector cpuUsageCollector = new RemoteCpuUsageCollector(Service.AUTH, "metrics/cpu");
double currentCpuUsage = cpuUsageCollector.get();
System.out.println("Current Remote CPU Usage: " + currentCpuUsage + "%");

⁠5.3 REST API Endpoints for Metrics Collection

⁠Example API Endpoints

⁠Example: Fetching CPU Usage from a Remote Service

curl -X GET http://service-instance:8080/metrics/cpu

⁠6. Adaptation Actions

⁠6.1 Available Adaptation Actions

⁠7. Summary

The Adaptable TeaStore Recommender service is a critical component for delivering personalized user experiences. It can be easily deployed using Docker commands or Docker Compose and integrates seamlessly with other services in the Adaptable TeaStore architecture. The provided Dockerfile ensures a well-configured environment for running the service, while the instrumented adaptation actions and metrics collectors enable effective adaptability and monitoring capabilities. For further details, refer to the Getting Started documentation⁠.